Ron Elber  Professor  ron@cs.cornell.edu  http://www.cs.cornell.edu/ron/old_webpage-ron/index_ron.html Ph.D. Hebrew Univ., Jerusalem, 1984   My research is in the field of Computational Molecular Biology. We develop computer algorithms to study sequences, structures, dynamics and function of proteins and apply these methods to a variety of biological problems. Our techniques are implemented in a single system MOIL available on the web.  Current research directions include: Mean field approaches for global optimization and structure prediction (Locally

Development of folding potentials using linear programming: The design of folding potentials relies on considerable human intuition and many trials and errors. I developed an automated protocol that
"learns" from experience and failures and constantly improves the quality of the current potential energy. The procedure is based on linear programming and exact manipulation of large amount of
experimental information is possible. We used about 30 million constraints to derive a new folding potential. We specifically design energy functions for which threading and folding are performed
efficiently and accurately. 

Extending the time scale of simulations. One of the striking observations in dynamics of biological molecules is the extremely large time scale they covered. Initiation by light absorption of biochemical processes is very rapid (10-15 seconds), while protein folding is slow (milliseconds to minutes). Current simulation approaches (Molecular Dynamics MD) are restricted to nanoseconds (10-9 seconds). I developed a stochastic path integral formulation that provides a numerically stable trajectory for almost an arbitrary time step. We apply the new algorithm to study activation of proteins (the R->T transitions in hemoglobin _microseconds) and to protein folding (folding of C peptide). The method provides systematic approximation to the dynamics and is more efficient than MD by orders of magnitude.

University Activities 

• Acting head of the NIH resource for parallel computing at the Cornell Theory Center. 
  Genomics Task Force. 

• A stochastic path approach to compute atomically detailed trajectories: Application to the
  folding of C peptide. Journal Phys. Chem. B 103 (1999), 899-911 (with J. Meller and R.
  Olender).  
• Fractal Analysis of Protein Potential Energy Landscapes. Phys. Rev. E 59 (1999),2231-2243 (with D. A. Lidar, D. Thirumalai, and R. B. Gerber).  
• Application of a stochastic path integral to the computations of an optimal path and ensembles of trajectories. Lecture Notes in Computational Science and Engineering 4 (P. Deuflhard, J. Hermans, B. Leimukhlar, A. E. Mark, S. Reich, R. D. Skeel, eds.)
•  Computational Molecular  Dynamics: Challenges, Methods, Ideas. Springer Verlag, Berlin Heidelberg, (1999), 263-280 (with B. Roux and R. Olender).  
• Knowledge-based structure prediction of MHC class I bound peptides: A study of twenty-three complexes. Folding and Design 3 (1998), 549-564 (with O. Schueler-Furman and H. Margalit).  
• Dynamics of peptide folding. Classical and Quantum Dynamics in Condensed Phase Simulations (B. Berne, G. Ciccotti, and D. Coker, eds.), World Scientific, Singapore, (1998), 423-444 (with D. Mohanty and C. Simmerling)